针对当前彩色图像和深度图像(RGB-D)特征融合困难、联合识别效率不高的问题,提出了一种结合K奇异值分解(KSVD)和最大相关最小冗余准则(mRMR)的RGB-D场景图像融合算法。该算法首先采用KSVD稀疏图像的特征,将稀疏系数对应的字典原子作为特征融合的参数,以完整地表达图像的全部信息;之后采用互信息的mRMR原则求取维度最小且各维度之间相关性最小的特征原子组合;最后通过最大化原则融合特征原子对应的稀疏系数,从而完成了两种图像之间的有效信息融合。实验结果表明,该算法在信息熵、互信息和边缘保持度等方面比主成分分析-K奇异值分解和非下采样轮廓变换-K奇异值分解融合算法更有优势,有效提高了图像目标的识别准确率和成功率。

To solve the problems of difficulty of feature fusion and low efficiency of joint recognition in color image and depth image(RGB-D), a new algorithm of RGB-D scene image fusion is proposed based on K singular value decomposition (KSVD) and maximum correlation minimum redundancy atoms (mRMR) principle. Firstly, the features of the sparse KSVD image and the dictionary atoms corresponding to the sparse coefficients are used as the parameters of feature fusion to fully express the whole information of image. Secondly, the mRMR principle of mutual information is used to determine the characteristic atom combination which has minimum dimensions and minimum correlation among different dimensions. Finally, the sparse coefficients are fused by the maximization principle to obtain the effective information fusion between two images. Experimental results show that the proposed algorithm has advantages over principal component analysis-K singular value decomposition and non-subsampled contour transform-K singular value decomposition fusion algorithms in terms of information entropy, mutual information and edge preservation, which improves recognition accuracy and success rate of the image targets effectively.